As the number of cameras and sensors in video surveillance systems constantly increases, it is a challenge to monitor all the cameras and sensors with a limited number of operators (e.g., human observers). Each operator can monitor only several cameras concurrently and the operator's attention typically degrades over time. Often the content of channels is repetitive with not much importance over long stretches of time (a channel may refer to, for example, a video stream or data from a single camera, sensor or source).
To aid the operators, current surveillance systems use various video analytics engines to detect preconfigured event types automatically and present the detected events to the operators. The video analytics engines may be preconfigured to detect events according to preset event types, e.g. intrusion detection, motion detection, people count, crowd management, camera tamper, etc. However, because these systems are only preconfigured for specific event types, any occurrence that is not preconfigured would not be detected.
Accordingly, there is a need in the art for a surveillance system that foresees and predicts relevant events to be detected, before they are configured by a user, and that prioritize between viewing channels and events.